English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-basedtechnology that offers a range of flexible configurations for massive IoT radioaccess from groups of devices with heterogeneous requirements. A configurationspecifies the amount of radio resources allocated to each group of devices forrandom access and for data transmission. Assuming no knowledge of the trafficstatistics, the problem is to determine, in an online fashion at eachTransmission Time Interval (TTI), the configurations that maximizes thelong-term average number of IoT devices that are able to both access anddeliver data. Given the complexity of optimal algorithms, a CooperativeMulti-Agent Deep Neural Network based Q-learning (CMA-DQN) approach isdeveloped, whereby each DQN agent independently control a configurationvariable for each group. The DQN agents are cooperatively trained in the sameenvironment based on feedback regarding transmission outcomes. CMA-DQN is seento considerably outperform conventional heuristic approaches based on loadestimation.

Cooperative Deep Reinforcement Learning for Multiple-Group NB-IoT Networks Optimization